In this work, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. Preliminary results of this work have been published in ISBI 2017 and Scientific Reports (SREP). After the SREP publication, the work had a press release, which caught the attention of the general media: Fox News, Daily Mail, Wired, The Australian (Section Counting the Decimal Places), Huffington Post, ComputerWorld, The Lead, TechExplore, Medical News Today, Engadget, Inside South Australia, Indaily, The Advertiser, NewsX, Gizmodo (India). Check these interviews: Luke’s live radio interview (Radio Adelaide), Lyle’s Researchgate interview.
Acoustic Surveillance System
Underwater noise has been identified by EU as a pollutant for biological species, including marine mammals, fish, invertebrates and to biodiversity as a whole. Despite the vast amount of scientific theoretical, laboratorial and experimental work, the extent of impact of noise on these species is not clear but its monitoring is a first step for control. The SUBECO project aims at developing a prediction and monitoring system of the underwater noise off the coast of continental Portugal. This is a must have tool to support the mission of protecting the marine environment but also for security and defense.
A Learned based method to design cost functions
DO is an innovative way of estimating a surrogate of the gradient of a “well behaved” cost function from data and solve a computer vision problems involving models (e.g. error functions) shaped by training data. This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, this paper proposes Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function.